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Showing all 12 results Save | Export
Mindorff, David – ProQuest LLC, 2023
Practical work involving laboratory experiments is agreed upon to be an essential component of secondary science education. Practical work encompasses a broad range of activity types. The different forms of practical work are not equal in terms of cognitive demand and learning benefit. Inquiry-based investigations provide experience of cognitive…
Descriptors: Secondary Education, Science Education, Biology, Information Retrieval
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Sainan Xu; Jing Lu; Jiwei Zhang; Chun Wang; Gongjun Xu – Grantee Submission, 2024
With the growing attention on large-scale educational testing and assessment, the ability to process substantial volumes of response data becomes crucial. Current estimation methods within item response theory (IRT), despite their high precision, often pose considerable computational burdens with large-scale data, leading to reduced computational…
Descriptors: Educational Assessment, Bayesian Statistics, Statistical Inference, Item Response Theory
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Pangrazio, Luci; Selwyn, Neil – Pedagogy, Culture and Society, 2021
The ongoing 'datafication' of contemporary society has a number of implications for schools and schooling. One is the increasing calls for schools to help develop young people's understandings about the role that digital data now plays in their everyday lives -- especially in terms of the 'data economy' and 'surveillance capitalism'. Reporting on…
Descriptors: Data Collection, Data Analysis, Technology Uses in Education, Data Processing
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Liu, Ran; Stamper, John; Davenport, Jodi – Journal of Learning Analytics, 2018
Temporal analyses are critical to understanding learning processes, yet understudied in education research. Data from different sources are often collected at different grain sizes, which are difficult to integrate. Making sense of data at many levels of analysis, including the most detailed levels, is highly time-consuming. In this paper, we…
Descriptors: Intelligent Tutoring Systems, Learning, Data Analysis, Student Development
Selent, Douglas; Patikorn, Thanaporn; Heffernan, Neil – Grantee Submission, 2016
In this paper, we present a dataset consisting of data generated from 22 previously and currently running randomized controlled experiments inside the ASSISTments online learning platform. This dataset provides data mining opportunities for researchers to analyze ASSISTments data in a convenient format across multiple experiments at the same time.…
Descriptors: Intelligent Tutoring Systems, Data, Randomized Controlled Trials, Electronic Learning
P. Janelle McFeetors – Sage Research Methods Cases, 2016
This case study describes an experience of using constructivist grounded theory to analyze data. The project investigated how high school students improved their approaches to learning mathematics. Over 4 months, students participated in processes which supported their learning while simultaneously generating data, including interactive writing,…
Descriptors: High School Students, Mathematics Education, Data Analysis, Data Interpretation
Liu, Ran; Stamper, John; Davenport, Jodi – Grantee Submission, 2018
Temporal analyses are critical to understanding learning processes, yet understudied in education research. Data from different sources are often collected at different grain sizes, which are difficult to integrate. Making sense of data at many levels of analysis, including the most detailed levels, is highly time-consuming. In this paper, we…
Descriptors: Intelligent Tutoring Systems, Learning, Data Analysis, Student Development
Snow, Erica L. – International Educational Data Mining Society, 2015
Intelligent tutoring systems are adaptive learning environments designed to support individualized instruction. The adaptation embedded within these systems is often guided by user models that represent one or more aspects of students' domain knowledge, actions, or performance. The proposed project focuses on the development and testing of user…
Descriptors: Intelligent Tutoring Systems, Models, Individualized Instruction, Needs Assessment
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Blagdanic, Casandra; Chinnappan, Mohan – Australian Mathematics Teacher, 2013
Numeracy in schools is becoming an increasingly important part of mathematics learning and teaching. This is because educators want students to engage with mathematical concepts more deeply, use mathematics to make sense of their environment and make decisions that are based on the analysis of mathematical information. In order to be numerate,…
Descriptors: Statistical Analysis, Statistics, Data Interpretation, Numeracy
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Omar, Ahmad Fairuz; MatJafri, Mohd Zubir – Physics Education, 2011
The Swift Turbidity Marker is an optical instrument developed to measure the level of water turbidity. The components and configuration selected for the system are based on common turbidity meter design concepts but use a simplified methodology to produce rapid turbidity measurements. This work is aimed at high school physics students and is the…
Descriptors: Optics, International Organizations, Data Analysis, Mathematical Applications
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Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection